Prediction of economic variables is a basic component not only for economic models, but also for many business decisions. But it is difficult to produce accurate predictions in times of economic crises, which cause nonlinear effects in the data. Such evidence appeared in the German automobile industry as a consequence of the financial crisis in 2008/09, which influenced exchange rates and a...

In this article the general non-symmetric parametric form of the incremental secant stiffness matrix for nonlinear analysis of solids have been investigated to present a semi analytical sensitivity analysis approach for geometric nonlinear shape optimization. To approach this aim the analytical formulas of secant stiffness matrix are presented. The models were validated and used to perform inve...

This study compared the use of five different models to describe the growth from birth to 20 weeks of age of kids from both genders of nondescript goats. Fifty nine (59) nondescript kids were weighed weekly at the university of Maiduguri livestock teaching and research farm, Nigeria and the live weights were modeled. Biologically relevant variables were estimated for each kid from the Logistic,...

Background and Objectives: Efficient cost management in hospitals’ pharmaceutical inventories have the potential to remarkably contribute to optimization of overall hospital expenditures. To this end, reliable forecasting models for accurate prediction of future pharmaceutical demands are instrumental. While the linear methods are frequently used for forecasting purposes chiefly due to their si...

This paper is a survey of SAS System features for nonlinear models, with emphasis on new features for nonlinear regression. Topics include automatic calculation of analytic derivatives, estimation with nonlinear parameter restrictions, tests of nonlinear hypotheses, maximum likelihood and generalized method of moments (GMM) estimation, estimation of simultaneous systems of nonlinear regression ...

evolving models have found applications in many real world systems. in this paper, potentials of the evolving linear models (elms) in tracking control design for nonlinear variable structure systems are introduced. at first, an elm is introduced as a dynamic single input, single output (siso) linear model whose parameters as well as dynamic orders of input and output signals can change through ...

in this study, a multi-objective genetic algorithm (moga) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear multi-input multi-output (mimo) systems. in the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. furthermore, se- curing low-level and high-level ...